Manual image annotation, such as defining and labelling regions of interest, is a fundamental processing stage of many research projects and industrial applications. In this paper, we introduce a simple and standalone manual image annotation tool: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. Due to its lightness and flexibility, the VIA software has quickly become an essential and invaluable research support tool in many academic disciplines. Furthermore, it has also been immensely popular in several industrial sectors which have invested in adapting this open source software to their requirements. Since its public release in 2017, the VIA software has been used more than 500, 000 times and has nurtured a large and thriving open source community.

The VGG Image Annotator (VIA).

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/1541-4337.12492

Application of Deep Learning in Food: A Review.

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat's identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

https://www.mdpi.com/1424-8220/19/22/4837/pdf

Deep Learning on Multi Sensor Data for Counter UAV Applications-A Systematic Review.

Within the world of machine learning there exists a wide range of different methods with respective advantages and applications. This paper seeks to present and discuss one such method, namely Convolutional Neural Networks (CNNs). CNNs are deep neural networks that use a special linear operation called convolution. This operation represents a key and distinctive element of CNNs, and will therefore be the focus of this method paper. The discussion starts with the theoretical foundations that underlie convolutions and CNNs. Then, the discussion proceeds to discuss some improvements and augmentations that can be made to adapt the method to estimate a wider set of function classes. The paper mainly investigates two ways of improving the method: by using locally connected layers, which can make the network less invariant to translation, and tiled convolution, which allows for the learning of more complex invariances than standard convolution. Furthermore, the use of the Fast Fourier Transform can improve the computational efficiency of convolution. Subsequently, this paper discusses two applications of convolution that have proven to be very effective in practice. First, the YOLO architecture is a state of the art neural network for image object classification, which accurately predicts bounding boxes around objects in images. Second, tumor detection in mammography may be performed using CNNs, accomplishing 7.2% higher specificity than actual doctors with only .3% less sensitivity. Finally, the invention of technology that outperforms humans in different fields also raises certain ethical and regulatory questions that are briefly discussed.

Deep Convolutional Neural Networksによる授業集中度評価システムの構築

Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat

This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

https://www.mdpi.com/2313-433X/4/5/63/pdf

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat.

A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging.

We investigate the potential and accuracy of snapshot hyperspectral imaging for authentication and classification of red-meat species. Snapshot hyperspectral images are acquired of lamb, beef, and pork samples. We consider 13 muscles types of standard loin and leg chops. We propose a deep 3D convolution neural network (CNN) architecture for extracting and classifying spectral-spatial learned features of red-meat. We present a comparison with state-of-the-art models including partial least-square discriminant analysis and support vector machines. Our results show that the proposed 3D-CNN model outperforms the state-of-the-art models with 95.81% and 96.01% for overall accuracy and average F 1 score, respectively. Visualization results show that the proposed 3D-CNN model is able to convert snapshot hyperspectral image data into an intelligent representation with accurate separation between red-meat types. This study opens the door for more research towards real-time and completely portable hyperspectral imaging systems due to the ability of snapshot hyperspectral cameras to work at video rate.

Mahmoud Al-Sarayreh

Papers

Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat.

A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging.

Deep Spectral-spatial Features of Snapshot Hyperspectral Images for Red-meat Classification